Datasets:
| license: ecl-2.0 | |
| task_categories: | |
| - text-classification | |
| - feature-extraction | |
| language: | |
| - en | |
| tags: | |
| - public | |
| - text | |
| - tabular | |
| - education | |
| - multivariate | |
| - time-series | |
| pretty_name: Smartphone and Smartwatch Activity and Biometrics 15m6 | |
| size_categories: | |
| - 1M<n<10M | |
| configs: | |
| - config_name: dataset | |
| data_files: | |
| - split: train | |
| path: | |
| - "phone_csv/accel/*.csv" | |
| - "phone_csv/gyro/*.csv" | |
| - "watch_csv/accel/*.csv" | |
| - "watch_csv/gyro/*.csv" | |
| # Smartphone and Smartwatch Activity and Biometrics 15m6 | |
| A 15.6-million-sample, multi-device time-series corpus that unites 3-axis accelerometer and gyroscope streams from 51 volunteers. Each participant carried a smartphone and smartwatch while performing 18 everyday activities for three minutes apiece, generating synchronized recordings sampled at 20 Hz. | |
| Every record is formatted as: | |
| - **subject_id** – integer 1600–1650 uniquely identifying the volunteer | |
| - **activity_code** – single ASCII letter A–S (the letter N is deliberately skipped) denoting the performed activity: | |
| - "A" - Walking | |
| - "B" - Jogging | |
| - "C" - Stairs | |
| - "D" - Sitting | |
| - "E" - Standing | |
| - "F" - Typing | |
| - "G" - Brushing Teet | |
| - "H" - Eating Soup | |
| - "I" - Eating Chips | |
| - "J" - Eating Pasta | |
| - "K" - Driking from Cup | |
| - "L" - Eating Sandwich | |
| - "M" - Kicking (Soccer Ball) | |
| - "O" - Playing Catch w/ Tennis Ball | |
| - "P" - Dribbling (Basketball) | |
| - "Q" - Writing | |
| - "R" - Clapping | |
| - "S" - Folding Clothes | |
| - **timestamp** – Unix epoch in seconds | |
| - **x, y, z** – instantaneous sensor readings for the corresponding device and modality | |
| The dataset is split into four directories—phone-accelerometer, phone-gyroscope, watch-accelerometer, watch-gyroscope—each holding 51 per-subject files. Alongside raw traces, pre-computed 10-second sliding-window features are provided, enabling both activity-recognition and behavioral-biometric research. | |
| **Acknowledgements**: The dataset is hosted at the [UCI MLR—Smartphone and Smartwatch Activity and Biometrics](https://archive.ics.uci.edu/dataset/507/wisdm+smartphone+and+smartwatch+activity+and+biometrics+dataset) |